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Record W4400498716 · doi:10.55037/lxlaser.21st.137

Enhancing Sensitivity And Quantifying Uncertainty Of Volumetric Two-Colour Two-Dye Laser-Induced Fluorescence Thermometry Of Aqueous Solutions

2024· article· en· W4400498716 on OpenAlexaff
Sina Kashanj, David S. Nobes

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemistry
TopicChemical Thermodynamics and Molecular Structure
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAqueous solutionFluorescenceSensitivity (control systems)Materials scienceLaserLaser-induced fluorescenceAnalytical Chemistry (journal)ChemistryOpticsChromatographyOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Laser-induced fluorescence (LIF) is a nonintrusive method for temperature field measurement in gaseous and liquid flow fields. There has been attempts to enhance the temperature sensitivity of this method mainly by applying two fluorescent dyes with opposite temperature sensitivity. While there has been advances in enhancing the temperature sensitivity of LIF thermometry in aqueous solutions, there factors that limits the enhancement of the temperature sensitivity. This work explores the factors that restricting the temperature sensitivity in the ratiometric systems using Fluorescein and Kiton red dyes. The uncertainty resources associated to this ratiometric system is also investigated considering the application of this method in both planar and volumetric configurations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.276
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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